Powell搜索法和惯性权重非线性调整局部收缩微粒群算法的混合算法  

A Hybrid Powell Search and Local Constriction Approach Particle Swarm Optimization with Nonlinear Varying Inertia Weight for Unconstrained Optimization

作　　者：刘国志[1] 苗晨[1] 

机构地区：[1]辽宁石油化工大学理学院,辽宁抚顺113001

出　　处：《吉林大学学报（理学版）》2008年第6期1149-1154,共6页Journal of Jilin University:Science Edition

基　　金：辽宁省自然科学基金(批准号:2004F100)

摘　　要：提出一种求解无约束最优化问题的新的混合算法Powell搜索法和惯性权重非线性调整局部收缩微粒群算法的混合算法.该算法不需要计算梯度,容易应用于实际问题中.通过对微粒群算法的修正,使混合算法具有更加精确和快速的收敛性.首先利用20个基准测试函数进行仿真计算比较,计算结果表明,新混合算法在求解质量和收敛速率上都优于其他算法(PSO,GPSO和NM-PSO算法).其次,将新混合算法和最新的各种协同PSO算法进行分析比较.结果表明,新混合算法在解的搜索质量、效率和关于初始点的鲁棒性方面都远优于其他算法.This paper proposes a hybrid algorithm （PowelI-NLCPSO） based on the Powell search method and the local constriction approach particle swarm optimization with nonlinear varying inertia weight for unconstrained optimization. Powell-NLCPSO is very easy to implement in practice since it does not require gradient computation. The modification of both the Powell search method and the particle swarm optimization intends to produce faster and more accurate convergence. The main purpose of the paper is to demonstrate how the standard particle swarm optimizers can be improved by incorporating a hybrid strategy. The computational results of a suit of 20 test function problems taken from the literature show that the hybrid Powell-NLCPSO approach outperforms other three relevant search techniques （i. e. , the original PSO, the guaranteed convergence particle swarm optimization （GCPSO） and hybrid NM-PSO） in terms of solution quality and convergence rate. In a later part of the comparative experiment, the Powell-NLCPSO algorithm is compared to evaluation. As evidenced by the overall assessment based on been demonstrated to be extremely effective and efficient computational experience, at locating best-practice the new algorithm has optimal solutions for unconstrained optimization.

关 键 词：POWELL搜索法 微粒群算法 无约束最优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]